Can our current data setup realistically support an AI-based pricing strategy within the next six months?

Short answer:
Based on what I know of our current data environment, implementing a robust AI-driven pricing strategy in six months is possible but will be challenging without first addressing some foundational gaps. Let me break this down carefully so we’re clear-eyed about what’s feasible.

1. What AI-Based Pricing Requires
To do this credibly, we need:
✅ Transaction-Level Data: Clean, detailed records of historical sales—volumes, prices, discounts, customer segments, time periods, and competitive context.
✅ Elasticity Insights: A track record showing how price changes influenced demand over time.
✅ Product and Customer Hierarchies: Standardized, maintained hierarchies that let us model price sensitivities across categories and segments.
✅ Data Freshness: Timely updates so we aren’t training models on stale or partial data.
✅ Governance: Clear ownership so everyone trusts the outputs enough to operationalize them.

2. Where We Stand Today
Realistically, here are the gaps I see:

• Fragmented Data: Our pricing, transaction, and customer data are spread across multiple systems, with inconsistent formats and taxonomies.

• Limited Historical Granularity: For some business units, we don’t have clean, SKU-level sales data going back far enough to build accurate elasticity models.

• Data Latency: Updates lag by weeks or longer in some divisions, which constrains timely re-pricing decisions.

• Governance and Ownership: We have work to do to align sales, finance, and product teams on shared definitions and accountability.

These are not insurmountable, but they’re significant if we expect AI recommendations to be trusted and acted upon.

3. What We’d Need to Change in the Next Six Months
To realistically support AI pricing within this timeframe, we’d need to:

1. Data Unification & Cleansing (First 8–10 weeks):

   • Consolidate transactional, discount, and customer data into a single repository (ideally a cloud data warehouse).

   • Standardize product and customer definitions across all systems.

   • Validate and reconcile any gaps or inconsistencies.

2. Historical Data Modeling (Parallel):

   • Reconstruct at least 2–3 years of clean historical transactions, if available, to train pricing elasticity models.

3. Architecture & Pipelines (Weeks 6–12):

   • Build or enhance data pipelines to update pricing-relevant data weekly or better.

4. Model Development and Testing (Weeks 10–20):

   • Develop AI models for elasticity, price optimization, and scenario simulation.

   • Pilot recommendations in a controlled environment to evaluate impact.

5. Change Management & Enablement (Weeks 18–24):

   • Train sales and pricing teams on how to interpret and use the AI outputs.

   • Establish governance: who validates, who approves, who executes.

4. Feasibility and Risks
If we prioritize this as a top strategic initiative, assign dedicated data engineering and analytics resources, and secure executive sponsorship to resolve data access issues quickly, it’s achievable to pilot AI-based pricing in a subset of products or regions in six months.

But it will require:

• Consistent focus (not treating this as an “add-on” project).

• Acknowledging that the first phase will be limited in scope—probably covering 20–30% of our portfolio rather than everything.

• A plan to iterate: the first models won’t be perfect, and refinement will take additional cycles.

Bottom Line:
Our current data setup isn’t ready today to fully operationalize AI-based pricing across the business in six months. But if we move decisively—prioritize data unification and governance immediately—we can credibly launch a targeted pilot within that timeframe.

I’d be happy to outline a more detailed project plan with resource estimates and dependencies so you can see exactly what it would take.